• Aberson, S. D., , M. L. Black, , R. A. Black, , J. J. Cione, , C. W. Landsea, , F. D. Marks, , and R. W. Burpee, 2006: Thirty years of tropical cyclone research with the NOAA P-3 aircraft. Bull. Amer. Meteor. Soc., 87, 10391055.

    • Search Google Scholar
    • Export Citation
  • Arnold, C. P., , Jr., and C. H. Dey, 1986: Observing-systems simulation experiments: Past, present, and future. Bull. Amer. Meteor. Soc., 67, 687695.

    • Search Google Scholar
    • Export Citation
  • Black, P. G., and Coauthors, 2007: Air–sea exchange in hurricanes. Bull. Amer. Meteor. Soc., 88, 357374.

  • Braun, S. A., , and W. K. Tao, 2000: Sensitivity of high-resolution simulations of Hurricane Bob (1991) to planetary boundary layer parameterizations. Mon. Wea. Rev., 128, 39413961.

    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., , and R. Rotunno, 2009: The maximum intensity of tropical cyclones in axisymmetric numerical model simulations. Mon. Wea. Rev., 137, 17701789.

    • Search Google Scholar
    • Export Citation
  • Davis, C., and Coauthors, 2008: Prediction of landfalling hurricanes with the advanced hurricane WRF model. Mon. Wea. Rev., 136, 19902005.

    • Search Google Scholar
    • Export Citation
  • DeMaria, M., , and J. Kaplan, 1994: A Statistical Hurricane Intensity Prediction Scheme (SHIPS) for the Atlantic Basin. Wea. Forecasting, 12, 209220.

    • Search Google Scholar
    • Export Citation
  • Donelan, M. A., , B. K. Haus, , N. Reul, , M. Stiassne, , H. C. Graber, , O. B. Brown, , and E. S. Saltzman, 2004: On the limiting aerodynamic roughness of the ocean in very strong winds. Geophys. Res. Lett., 31, L18306, doi:10.1029/2004GL019460.

    • Search Google Scholar
    • Export Citation
  • Fierro, A. O., , R. F. Rogers, , F. D. Marks, , and D. S. Nolan, 2009: The impact of horizontal grid spacing on the microphysical and kinematic structures of strong tropical cyclones simulated with the WRF-ARW model. Mon. Wea. Rev., 137, 37173743.

    • Search Google Scholar
    • Export Citation
  • Franklin, J. L., , M. L. Black, , and K. Valde, 2003: GPS dropwindsonde wind profiles in hurricanes and their operational implications. Wea. Forecasting, 18, 3244.

    • Search Google Scholar
    • Export Citation
  • Harper, B., , J. Kepert, , and J. Ginger, 2009: Guidelines for converting between various wind averaging periods in tropical cyclone conditions. WMO Tech. Rep. TCM-VI/Doc. 2.3, 64 pp.

    • Search Google Scholar
    • Export Citation
  • Hill, K. A., , and G. M. Lackmann, 2009: Analysis of idealized tropical cyclone simulations using the weather research and forecasting model: Sensitivity to turbulence parameterization and grid spacing. Mon. Wea. Rev., 137, 745765.

    • Search Google Scholar
    • Export Citation
  • Hock, T. F., , and J. L. Franklin, 1999: The NCAR GPS dropwindsonde. Bull. Amer. Meteor. Soc., 80, 407420.

  • Hong, S.-Y., , Y. Noh, , and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 23182341.

    • Search Google Scholar
    • Export Citation
  • Jarvinen, B. R., , C. J. Neumann, , and M. A. S. Davis, 1984: A tropical cyclone data tape for the North Atlantic Basin, 1886–1983: Contents, limitations, and uses. NOAA Tech. Rep. 22, NOAA/NWS/National Hurricane Center, Miami, FL, 21 pp.

    • Search Google Scholar
    • Export Citation
  • Kepert, J., 2001: The dynamics of boundary layer jets within the tropical cyclone core. Part I: Linear theory. J. Atmos. Sci., 58, 24692484.

    • Search Google Scholar
    • Export Citation
  • Kepert, J., , and Y. Wang, 2001: The dynamics of boundary layer jets within the tropical cyclone core. Part II: Nonlinear enhancement. J. Atmos. Sci., 58, 24852501.

    • Search Google Scholar
    • Export Citation
  • Knaff, J. A., , and R. M. Zehr, 2007: Reexamination of tropical cyclone wind-pressure relationships. Wea. Forecasting, 22, 7188.

  • Krayer, W. R., , and R. D. Marshall, 1992: Gust factors applied to hurricane winds. Bull. Amer. Meteor. Soc., 73, 613618.

  • Landsea, C. W., and Coauthors, 2004: A reanalysis of Hurricane Andrew’s intensity. Bull. Amer. Meteor. Soc., 85, 16991712.

  • Massey, F. J., 1951: The Kolmogorov–Smirnov test for goodness of fit. J. Amer. Stat. Assoc., 46, 6878.

  • Merceret, F. J., 1983: First order autoregressive low-pass filters: A user’s quick reference handbook. NOAA Tech. Rep. ERL RFC-9, NOAA/Environmental Research Laboratories, 25 pp.

    • Search Google Scholar
    • Export Citation
  • National Weather Service, cited 2010: Tropical Cyclone Definitions. NWSI 10-604, Tropical Cyclone Weather Services Program, Washington, DC. [Available online at http://www.nws.noaa.gov/directives/sym/pd01006004curr.pdf.]

    • Search Google Scholar
    • Export Citation
  • Nguyen, V. S., , R. K. Smith, , and M. T. Montgomery, 2008: Tropical-cyclone intensification and predictability in three dimensions. Quart. J. Roy. Meteor. Soc., 134, 563582.

    • Search Google Scholar
    • Export Citation
  • Nolan, D. S., , J. A. Zhang, , and D. P. Stern, 2009a: Evaluation of planetary boundary layer parameterizations in tropical cyclones by comparison of in situ observations and high-resolution simulations of Hurricane Isabel (2003). Part I: Initialization, maximum winds, and the outer-core boundary layer. Mon. Wea. Rev., 137, 36513674.

    • Search Google Scholar
    • Export Citation
  • Nolan, D. S., , J. A. Zhang, , and D. P. Stern, 2009b: Evaluation of planetary boundary layer parameterizations in tropical cyclones by comparison of in situ observations and high-resolution simulations of Hurricane Isabel (2003). Part II: Inner-core boundary layer and eyewall structure. Mon. Wea. Rev., 137, 36753698.

    • Search Google Scholar
    • Export Citation
  • OFCM, cited 2007: Interagency strategic research plan for tropical cyclones—The way ahead. Tech. Rep. FCM-P36-2007, Office of the Federal Coordinator for Meteorological Services and Supporting Research, Silver Spring, MD. [Available online at http://www.ofcn.gov/p36-isrtc/fcm-p36.htm.]

    • Search Google Scholar
    • Export Citation
  • Persing, J., , and M. T. Montgomery, 2003: Hurricane superintensity. J. Atmos. Sci., 60, 23492371.

  • Powell, M. D., , P. P. Dodge, , and M. L. Black, 1991: The landfall of Hurricane Hugo in the Carolinas: Surface wind distribution. Wea. Forecasting, 6, 379399.

    • Search Google Scholar
    • Export Citation
  • Powell, M. D., , E. W. Uhlhorn, , and J. D. Kepert, 2009: Estimating maximum surface winds from hurricane reconnaissance measurements. Wea. Forecasting, 24, 868883.

    • Search Google Scholar
    • Export Citation
  • Rogers, R., , and E. Uhlhorn, 2008: Observations of the structure and evolution of surface and flight-level wind asymmetries in Hurricane Rita (2005). Geophys. Res. Lett., 35, L22811, doi:10.1029/2008GL034774.

    • Search Google Scholar
    • Export Citation
  • Rotunno, R., , Y. Chen, , W. Wang, , C. Davis, , J. Dudhia, , and G. J. Holland, 2009: Large-eddy simulation of an idealized tropical cyclone. Bull. Amer. Meteor. Soc., 90, 17831788.

    • Search Google Scholar
    • Export Citation
  • Shapiro, L. J., 1983: The asymmetric boundary layer flow under a translating hurricane. J. Atmos. Sci., 40, 19841998.

  • Skamarock, W. C., 2004: Evaluating mesoscale NWP models using kinetic energy spectra. Mon. Wea. Rev., 132, 30193032.

  • Skamarock, W. C., , J. B. Klemp, , J. Dudhia, , D. O. Gill, , D. M. Barker, , W. Wang, , and J. G. Powers, 2005: A description of the Advanced Research WRF Version 2. NCAR Tech. Note TN-658+STR, 88 pp.

    • Search Google Scholar
    • Export Citation
  • Solow, A. R., 2010: On the maximum observed wind speed in a randomly sampled hurricane. J. Climate, 23, 12621265.

  • Stern, D. P., , and D. S. Nolan, 2009: Reexamining the vertical structure of tangential winds in tropical cyclones: Observations and theory. J. Atmos. Sci., 66, 35793600.

    • Search Google Scholar
    • Export Citation
  • Uhlhorn, E. W., , P. G. Black, , J. L. Franklin, , M. Goodberlet, , J. Carswell, , and A. S. Goldstein, 2007: Hurricane surface wind measurements from an operational stepped frequency microwave radiometer. Mon. Wea. Rev., 135, 30703085.

    • Search Google Scholar
    • Export Citation
  • Velden, C., and Coauthors, 2006: The Dvorak tropical cyclone intensity estimation technique: A satellite-based method that has endured for over 30 years. Bull. Amer. Meteor. Soc., 87, S6S9.

    • Search Google Scholar
    • Export Citation
  • Vickery, P. J., , and P. F. Skerlj, 2005: Hurricane gust factors revisited. J. Struct. Eng., 131, 825832.

  • Yau, M. K., , Y. Liu, , D. Zhang, , and Y. Chen, 2004: A multiscale numerical study of Hurricane Andrew (1992). Part VI: Small-scale inner-core structures and wind streaks. Mon. Wea. Rev., 132, 14101433.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 39 39 14
PDF Downloads 30 30 10

Observational Undersampling in Tropical Cyclones and Implications for Estimated Intensity

View More View Less
  • 1 NOAA/AOML/Hurricane Research Division, Miami, Florida
  • | 2 Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida
© Get Permissions
Restricted access

Abstract

The maximum surface wind speed is an important parameter for tropical cyclone operational analysis and forecasting, since it defines the intensity of a cyclone. Operational forecast centers typically refer the wind speed to a maximum 1- or 10-min averaged value. Aircraft reconnaissance provides measurements of surface winds; however, because of the large variation of winds in the eyewall, it remains unclear to what extent observing the maximum wind is limited by the sampling pattern. Estimating storm intensity as simply the maximum of the observed winds is generally assumed by forecasters to underestimate the true storm intensity. The work presented herein attempts to quantify this difference by applying a methodology borrowed from the observing system simulation experiment concept, in which simulated “observations” are drawn from a numerical model. These “observations” may then be compared to the actual peak wind speed of the simulation. By sampling a high-resolution numerical simulation of Hurricane Isabel (2003) with a virtual aircraft equipped with a stepped-frequency microwave radiometer flying a standard “figure-four” pattern, the authors find that the highest wind observed over a flight typically underestimates the 1-min averaged model wind speed by 8.5% ± 1.5%. In contrast, due to its corresponding larger spatial scale, the 10-min averaged maximum wind speed is far less underestimated (1.5% ± 1.7%) using the same sampling method. These results support the National Hurricane Center’s practice, which typically assumes that the peak 1-min wind is somewhat greater than the highest observed wind speed over a single reconnaissance aircraft mission.

Corresponding author address: Dr. Eric W. Uhlhorn, NOAA/AOML/Hurricane Research Division, 4301 Rickenbacker Cswy., Miami, FL 33149. E-mail: eric.uhlhorn@noaa.gov

Abstract

The maximum surface wind speed is an important parameter for tropical cyclone operational analysis and forecasting, since it defines the intensity of a cyclone. Operational forecast centers typically refer the wind speed to a maximum 1- or 10-min averaged value. Aircraft reconnaissance provides measurements of surface winds; however, because of the large variation of winds in the eyewall, it remains unclear to what extent observing the maximum wind is limited by the sampling pattern. Estimating storm intensity as simply the maximum of the observed winds is generally assumed by forecasters to underestimate the true storm intensity. The work presented herein attempts to quantify this difference by applying a methodology borrowed from the observing system simulation experiment concept, in which simulated “observations” are drawn from a numerical model. These “observations” may then be compared to the actual peak wind speed of the simulation. By sampling a high-resolution numerical simulation of Hurricane Isabel (2003) with a virtual aircraft equipped with a stepped-frequency microwave radiometer flying a standard “figure-four” pattern, the authors find that the highest wind observed over a flight typically underestimates the 1-min averaged model wind speed by 8.5% ± 1.5%. In contrast, due to its corresponding larger spatial scale, the 10-min averaged maximum wind speed is far less underestimated (1.5% ± 1.7%) using the same sampling method. These results support the National Hurricane Center’s practice, which typically assumes that the peak 1-min wind is somewhat greater than the highest observed wind speed over a single reconnaissance aircraft mission.

Corresponding author address: Dr. Eric W. Uhlhorn, NOAA/AOML/Hurricane Research Division, 4301 Rickenbacker Cswy., Miami, FL 33149. E-mail: eric.uhlhorn@noaa.gov
Save